Masashi Takeuchi1, Takumi Seto2, Masahiro Hashimoto3, Nao Ichihara4,5, Yosuke Morimoto1, Hirofumi Kawakubo1, Tatsuya Suzuki3, Masahiro Jinzaki3, Yuko Kitagawa1, Hiroaki Miyata4,5, Yasubumi Sakakibara6. 1. Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. 2. Department of Biosciences and Informatics, Keio University, 3-13-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan. 3. Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. 4. Department of Health Policy Management, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. 5. Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. 6. Department of Biosciences and Informatics, Keio University, 3-13-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan. yasu@bio.keio.ac.jp.
Abstract
BACKGROUND: Because cancers of hollow organs such as the esophagus are hard to detect even by the expert physician, it is important to establish diagnostic systems to support physicians and increase the accuracy of diagnosis. In recent years, deep learning-based artificial intelligence (AI) technology has been employed for medical image recognition. However, no optimal CT diagnostic system employing deep learning technology has been attempted and established for esophageal cancer so far. PURPOSE: To establish an AI-based diagnostic system for esophageal cancer from CT images. MATERIALS AND METHODS: In this single-center, retrospective cohort study, 457 patients with primary esophageal cancer referred to our division between 2005 and 2018 were enrolled. We fine-tuned VGG16, an image recognition model of deep learning convolutional neural network (CNN), for the detection of esophageal cancer. We evaluated the diagnostic accuracy of the CNN using a test data set including 46 cancerous CT images and 100 non-cancerous images and compared it to that of two radiologists. RESULTS: Pre-treatment esophageal cancer stages of the patients included in the test data set were clinical T1 (12 patients), clinical T2 (9 patients), clinical T3 (20 patients), and clinical T4 (5 patients). The CNN-based system showed a diagnostic accuracy of 84.2%, F value of 0.742, sensitivity of 71.7%, and specificity of 90.0%. CONCLUSIONS: Our AI-based diagnostic system succeeded in detecting esophageal cancer with high accuracy. More training with vast datasets collected from multiples centers would lead to even higher diagnostic accuracy and aid better decision making.
BACKGROUND: Because cancers of hollow organs such as the esophagus are hard to detect even by the expert physician, it is important to establish diagnostic systems to support physicians and increase the accuracy of diagnosis. In recent years, deep learning-based artificial intelligence (AI) technology has been employed for medical image recognition. However, no optimal CT diagnostic system employing deep learning technology has been attempted and established for esophageal cancer so far. PURPOSE: To establish an AI-based diagnostic system for esophageal cancer from CT images. MATERIALS AND METHODS: In this single-center, retrospective cohort study, 457 patients with primary esophageal cancer referred to our division between 2005 and 2018 were enrolled. We fine-tuned VGG16, an image recognition model of deep learning convolutional neural network (CNN), for the detection of esophageal cancer. We evaluated the diagnostic accuracy of the CNN using a test data set including 46 cancerous CT images and 100 non-cancerous images and compared it to that of two radiologists. RESULTS: Pre-treatment esophageal cancer stages of the patients included in the test data set were clinical T1 (12 patients), clinical T2 (9 patients), clinical T3 (20 patients), and clinical T4 (5 patients). The CNN-based system showed a diagnostic accuracy of 84.2%, F value of 0.742, sensitivity of 71.7%, and specificity of 90.0%. CONCLUSIONS: Our AI-based diagnostic system succeeded in detecting esophageal cancer with high accuracy. More training with vast datasets collected from multiples centers would lead to even higher diagnostic accuracy and aid better decision making.
Entities:
Keywords:
Computed tomography; Convolutional neural network; Esophageal cancer
Authors: Sue J Fu; Vanessa P Ho; Jennifer Ginsberg; Yaron Perry; Conor P Delaney; Philip A Linden; Christopher W Towe Journal: Minim Invasive Surg Date: 2016-12-08